Single-cell RNA sequencing of bone marrow celld from patients with paroxysmal nocturnal hemoglobinuria.
Single-cell RNA-seq · human · 46 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 46 samples: 46 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Imputing causality and clonal dynamics from single-cell transcriptomics in paroxysmal nocturnal hemoglobinuriaLeukemia · 2026
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE248025_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1041418 and SRA study SRP472463. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 46 sample titles: 46 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM7904817 Healthy10_CD34positive_cells_GEM
- GSM7904818 Healthy10_Linpositive_cells_GEM
- GSM7904819 Healthy11_CD34positive_cells_GEM
- GSM7904820 Healthy11_Linpositive_cells_GEM
- GSM7904821 Healthy7_CD34positive_cells_GEM
- GSM7904822 Healthy7_Linpositive_cells_GEM
- GSM7904823 Healthy8_CD34positive_cells_GEM
- GSM7904824 Healthy8_Linpositive_cells_GEM
- GSM7904825 Healthy9_CD34positive_cells_GEM
- GSM7904826 Healthy9_Linpositive_cells_GEM
- GSM7904827 PNH10_CD34positive_PNH-type_GEM
- GSM7904828 PNH10_CD34positive_Wild-type_GEM
- GSM7904829 PNH10_Linpositive_PNH-type_GEM
- GSM7904830 PNH10_Linpositive_Wild-type_GEM
- GSM7904831 PNH11_CD34positive_PNH-type_GEM
- GSM7904832 PNH11_CD34positive_Wild-type_GEM
- GSM7904833 PNH11_Linpositive_PNH-type_GEM
- GSM7904834 PNH11_Linpositive_Wild-type_GEM
- GSM7904835 PNH4_CD34positive_PNH-type_GEM
- GSM7904836 PNH4_CD34positive_Wild-type_GEM
- GSM7904837 PNH4_Linpositive_PNH-type_GEM
- GSM7904838 PNH4_Linpositive_Wild-type_GEM
- GSM7904839 PNH5_CD34positive_PNH-type_GEM
- GSM7904840 PNH5_CD34positive_Wild-type_GEM
- GSM7904841 PNH5_Linpositive_PNH-type_GEM
- GSM7904842 PNH5_Linpositive_Wild-type_GEM
- GSM7904843 PNH6-1_CD34positive_PNH-type_GEM
- GSM7904844 PNH6-1_CD34positive_Wild-type_GEM
- GSM7904845 PNH6-1_Linpositive_PNH-type_GEM
- GSM7904846 PNH6-1_Linpositive_Wild-type_GEM
- GSM7904847 PNH6-2_CD34positive_PNH-type_GEM
- GSM7904848 PNH6-2_CD34positive_Wild-type_GEM
- GSM7904849 PNH6-2_Linpositive_PNH-type_GEM
- GSM7904850 PNH6-2_Linpositive_Wild-type_GEM
- GSM7904851 PNH7_CD34positive_PNH-type_GEM
- GSM7904852 PNH7_CD34positive_Wild-type_GEM
- GSM7904853 PNH7_Linpositive_PNH-type_GEM
- GSM7904854 PNH7_Linpositive_Wild-type_GEM
- GSM7904855 PNH8_CD34positive_PNH-type_GEM
- GSM7904856 PNH8_CD34positive_Wild-type_GEM
+ 6 more — browse all 46 samples with per-sample file links →
- GSE271107 Single-cell RNA sequencing of bone marrow aspirate samples from multiple myeloma and its precursor conditions 19 samples shares single-cell, marrow, bone
- GSE253355 Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics 12 samples shares single-cell, marrow, bone
- GSE305558 Single-cell profiling reveals RAB13+ endothelial cells and profibrotic mesenchymal cells in aged human bone marrow 12 samples shares single-cell, marrow, bone
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE287301 Single-cell RNA-sequencing of HNSCC-infiltrating T cells 48 samples shares single-cell
- GSE269877 Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway 38 samples shares single-cell
- GSE291890 Single-Cell RNA Sequencing Unveils Macrophage Involvement in Aortic Valve Stenosis Progression 33 samples shares single-cell
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.